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Quantifying circular RNA expression from RNA-seq data using model-based framework
Author(s) -
Musheng Li,
Xueying Xie,
Jing Zhou,
Mengying Sheng,
Xiaofeng Yin,
Eun-A Ko,
Tong Zhou,
Wanjun Gu
Publication year - 2017
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btx129
Subject(s) - circular rna , computational biology , rna , rna seq , ribosomal rna , biology , gene expression , expression (computer science) , computer science , algorithm , gene , transcriptome , genetics , programming language
Circular RNAs (circRNAs) are a class of non-coding RNAs that are widely expressed in various cell lines and tissues of many organisms. Although the exact function of many circRNAs is largely unknown, the cell type-and tissue-specific circRNA expression has implicated their crucial functions in many biological processes. Hence, the quantification of circRNA expression from high-throughput RNA-seq data is becoming important to ascertain. Although many model-based methods have been developed to quantify linear RNA expression from RNA-seq data, these methods are not applicable to circRNA quantification.

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